ArticleOphthalmology science2026
Deep Learning Analysis of Widefield Cornea Endothelial Imaging in Fuchs Dystrophy.
Article in Ophthalmology science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Recent advances in corneal specular microscopy image analysis through artificial intelligence.PLOS digital health · 2026Review
- Longitudinal Study ofMedical sciences (Basel, Switzerland) · 2026Article
- Artificial intelligence in corneal endothelial disease and endothelial keratoplasty: current evidence, clinical readiness, and future directions.Frontiers in ophthalmology · 2026Review
- Artificial Intelligence Application in Cornea and External Diseases.Diagnostics (Basel, Switzerland) · 2025Review
- Deep learning-assisted widefield endothelial imaging in Descemet membrane endothelial keratoplasty.Frontiers in medicine · 2025Article
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To evaluate the use of a deep learning network (DLN) in analyzing widefield specular microscopy (WFSM) images in eyes with Fuchs endothelial corneal dystrophy (FECD). Design: Cross-sectional clinical observational study. Participants: A total of 1839 images were obtained via WFSM imaging (CEM-530, Nidek Co Ltd) performed on 155 FECD eyes. A separate data set comprising images from 50 FECD eyes and 50 control eyes (70% training, 30% validation) was used for DLN training, which was tested on 354 images from 55 eyes from varying regions (central, paracentral, and peripheral). Methods: Images were graded based on a standardized quality score. Central images were compared with paracentral and peripheral images in terms of quality and morphometric parameters: endothelial cell density (ECD), coefficient of variation (CV), and hexagonality (HEX). A U-Net-based DLN was developed and trained using the separate data set and then tested on an external longitudinal data set (baseline and 1 month). Segmentation accuracy between DLN and manual analysis was compared using the Sørensen-Dice coefficient. Morphometric outcomes (ECD, HEX, and CV) were analyzed using paired Main Outcome Measures: Intergrader agreement for image quality and FECD severity; comparison of DLN-derived ECD with manual analysis. Results: Strong intergrader agreement was observed for both image quality (κ = 0.967, 95% confidence interval [CI]: 0.959-0.976) and FECD severity (κ = 0.891, 95% CI: 0.786-0.995). Endothelial cell density differences between paracentral/peripheral regions were significant in eyes without or with subclinical edema ( Conclusions: This study presents a novel application of deep learning for analyzing widefield corneal endothelial images. The integration of a progression visualization tool enhances interpretability, allowing efficient autoanalysis and organization of large WFSM data sets-streamlining workflows and addressing limitations of manual interpretation. Financial Disclosures: The authors have no proprietary or commercial interest in any materials discussed in this article.
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